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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Allam Balaram Sajja Suneel P.M. Kavitha Achinta Saikia S. Gopi Y dileep Kumar Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of Computer Science and Applications SRM Institute of Science And Technology Ramapuram Chennai-89 Department of Statistics Mariani College Mariani Jorhat Assam Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of ECE School of Engineering Mohan Babu University Tirupati

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In the field of cloud computing, ensuring secure and efficient key agreement among multiple parties has emerged as a paramount challenge. Traditional key agreement protocols often rely on central authorities or trusted third parties, posing significant security and privacy concerns. To address these challenges, this paper introduces a novel key agreement protocol designed specifically for cloud computing platforms, emphasizing security, efficiency, and resilience without depending on a trusted third party. The proposed protocol innovatively combines distributed Key Generation (dKG) with a dynamic Consensus Mechanism, Zero-Knowledge Proof (ZKP) based authentication, and a Multi-Cloud redundancy approach, offering a comprehensive solution to secure multi-party communication in distributed cloud environments. The dKG protocol facilitates the collaborative generation of a shared secret among participants, significantly enhancing security by eliminating single points of failure. The proposed dynamic Consensus Mechanism ensures the integrity and finality of key agreement transactions on a blockchain-based ledger, adapting to network conditions and participant trust levels to optimize performance without compromising security. ZKP-based authentication allows participants to verify their identities without revealing sensitive information, preserving privacy and thwarting impersonation attacks. Lastly, the Multi-Cloud redundancy strategy enhances the protocol's resilience to cloud-specific vulnerabilities and service outages, ensuring high availability and robustness.

关键词： Cloud computing Privacy redundancy Authentication Impersonation attacks robustness Consensus protocol

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Strengthening Authentication: The role of Multi-Level Hashing in OSPA Protocol

Strengthening Authentication: The Role of Multi-Level Hashin...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Hariharasudhan Vineetha Varghese K Malarvizhi S.P. Santhoshkumar T. Nalini d. Vikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Computer Science al Business Systems Jct College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of computer science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Artificial Intelligence Data Science Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture.

关键词： Hash functions resistance Protocols Heuristic algorithms Authentication Passwords Universal Serial Bus Computer crime Intelligent systems Eavesdropping

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Advancements and Applications in Object detection and Pattern recognition

Advancements and Applications in Object Detection and Patter...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Govinda rajulu G Savija. J Shereen Priscila S T.P. Anish M. Nalini Siva Subramanian r Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Artificial Intelligence and Data Science Velammal Engineering College Chennai Department of IT Easwari Engineering College Chennai Dept of CSE R.M.K. College of Engineering and Technology Tiruvallur India Dept of CSE S A Engineering College Dept of CSE R.M.K College of Engg and Tech

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Object detection and pattern detection are fundamental problems in computer vision, and have real-world uses such as in autonomous vehicles, healthcare, and security. Over the recent years, improvements made in the deep learning framework have democratized these fields and made it easier for researchers to build accurate models. CNN has become very popular in object detection and is currently implemented through features architectures like Faster rCNN and the Single Shot MultiBox detector (SSd). When it comes to pattern recognition, especially in sequential data, recurrent Neural Networks (rNN), especially Long Short- Term Memory (LSTM) network outperforms the rest. However, some of the important issues that remain unresolved include issues of robustness to varying environmental conditions and the question of computation cost. From the results and analysis of the research, it can be concluded that the following directions for future studies can be distinguished, enhance the model's robustness with methods such as domain adaptation, create new, less complex architectures for real-time applications. Also, multi-modal fusion and unsupervised learning are the two areas with a brighter future in the field of automating machine learning. In sum, more advanced object detection and pattern recognition will continuously push the development and usability of these technologies for safer, more efficient, and intelligent systems at different domains.

关键词： deep learning Adaptation models Accuracy recurrent neural networks Computational modeling Object detection data models robustness Pattern recognition Security

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Federated Learning in big data with IoT for Intrusion detection

Federated Learning in Big Data with IoT for Intrusion Detect...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Anitha Govindaram J. S. Prasath A. Suganya K. Jayasakthi N. rajkumar Jose Anand A. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Bengaluru Karnataka Department of Artificial Intelligence and Data Science Anand Institute of Higher Technology Chengalpattu Tamil Nadu Department of ECE KCG College of Technology Chennai Tamil Nadu Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Artificial Intelligence (AI) part of Federated Learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has not yet gained mainstream adoption, mostly because of concerns about privacy and security. Security and privacy issues with the FL technique need to be found, examined, and recorded before at this point, we can expect to see significant progress in this field of study or widespread adoption of the FL approach. This article aims to afford a complete review of the methods discussed with FL for huge data by introducing studies from six angles: blockchain, privacy, hybrid approach, VFL, FL, and foundation design and device heterogeneity. This survey can address the challenges of FL, offer their predictions for the future of research on the subject, outline how FL is now being put to use in the real world, and with most salient features.

关键词： Surveys Privacy data privacy Federated learning reviews Intrusion detection Learning (artificial intelligence) Security Informatics Mobile computing

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Hard Sample Matters a Lot in Zero-Shot Quantization

Hard Sample Matters a Lot in Zero-Shot Quantization

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Conference on Computer Vision and Pattern recognition (CVPr)

作者： Huantong Li Xiangmiao Wu Fanbing Lv daihai Liao Thomas H. Li Yonggang Zhang Bo Han Mingkui Tan South China University of Technology Information Technology R&D Innovation Center of Peking University Changsha Hisense Intelligent System Research Institute Co. Ltd School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Hong Kong Baptist University Key Laboratory of Big Data and Intelligent Robot Ministry of Education PengCheng Laboratory

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data.

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Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised domain Adaptation

arXiv

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arXiv 2022年

作者： Lin, Hongbin Zhang, Yifan Qiu, Zhen Niu, Shuaicheng Gan, Chuang Liu, Yanxia Tan, Mingkui South China University of Technology China National University of Singapore Singapore Information Technology R&D Innovation Center Peking University China MIT-IBM Watson AI Lab United States Key Laboratory of Big Data and Intelligent Robot Ministry of Education China

This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised domain Adaptation (CI-UdA), where the labeled source domain contains all classes, but the classes in the unlabeled target domain increase sequentially. This problem is challenging due to two difficulties. First, source and target label sets are inconsistent at each time step, which makes it difficult to conduct accurate domain alignment. Second, previous target classes are unavailable in the current step, resulting in the forgetting of previous knowledge. To address this problem, we propose a novel Prototype-guided Continual Adaptation (ProCA) method, consisting of two solution strategies. 1) Label prototype identification: we identify target label prototypes by detecting shared classes with cumulative prediction probabilities of target samples. 2) Prototype-based alignment and replay: based on the identified label prototypes, we align both domains and enforce the model to retain previous knowledge. With these two strategies, ProCA is able to adapt the source model to a class-incremental unlabeled target domain effectively. Extensive experiments demonstrate the effectiveness and superiority of ProCA in resolving CI-UdA. The source code is available at https://***/Hongbin98/***. Copyright © 2022, The Authors. All rights reserved.

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Increasing Octupole Collectivity across the Z=64 Isotopic Chain: B(E3) Values in Gd150

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Physical review Letters 2025年第9期134卷 092501-092501页

作者： S. Pascu E. Yüksel Abhishek P. Stevenson G. H. Bhat r. N. Mao K. Nomura C. Costache Z. P. Li School of Mathematics and Physics University of Surrey Guildford GU2 7XH United Kingdom “Horia Hulubei” National Institute for R&D in Physics and Nuclear Engineering R-77125 Bucharest-Magurele Romania Department of Higher Education (GDC Shopian) Jammu and Kashmir 192 303 India School of Physical Science and Technology Southwest University Chongqing 400715 China Department of Physics Hokkaido University Sapporo 060-0810 Japan Nuclear Reaction Data Center Hokkaido University Sapporo 060-0810 Japan

An enhanced electric octupole (E3) strength of 45(5) W.u. has been measured for the 3−→0+ transition in Gd150, providing direct evidence for large octupole collectivity in this nucleus. The value was determined following two sensitive experiments, to measure the mean lifetime of the Jπ=3− level and the weak E3 decay branch direct to the ground state. The systematics of the E3 strength across the Gd isotopic chain reveals that enhanced octupole collectivity is obtained for Gd150. The increasing low-lying octupole strength in the Gd isotopes is analyzed with five state-of-the-art theoretical models. It is found to be best reproduced by quasiparticle random phase approximation calculations, providing a good understanding of octupole collectivity in the rare-earth region.

关键词： Gadolinium alloys

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ELCCFd: An Efficient and Enhanced Credit Card Fraud detection using Enhanced deep Learning Principle

ELCCFD: An Efficient and Enhanced Credit Card Fraud Detectio...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： Pandarinath Potluri Sajja Suneel B. Chandra d. Muthukumaran Makarand Upadhyaya r rajesh Kanna department of CSE Swarnandhra college of engineering and technology Sitarampuram Narsapuram West Godavari district Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Management Studies Vignan's Institute of Information Technology(A) AP India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Management & Marketing College of Business Administration University of Bahrain Department of Computer Science CHRIST (Deemed to be University)

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud detection (ELCCFd) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN’s output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFd methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems.

关键词： deep learning Accuracy Protocols Image processing Merging Credit cards Cleaning

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Identification of cell type-specific cell-penetrating peptides through in vivo phage display leveraged by next generation sequencing

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Biomedicine and Pharmacotherapy 2025年 182卷 117740-117740页

作者： Kohl, Franziska Laufkötter, Oliver Firth, Mike Krimpenfort, Luc Mangla, Priyanka Ansarizadeh, Mohammadhassan Geylan, Gökçe Eklund, Lauri de Maria, Leonardo Jakobsson, Lars Wiseman, John Centre for Genomics Research Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden Department of Life Science Informatics B-IT Rheinische Friedrich-Wilhelms-Universität Bonn Germany Data Sciences and Quantitative Biology Discovery Sciences BioPharmaceuticals R&D AstraZeneca Cambridge United Kingdom Oligonucleotides and Targeted Delivery Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Oulu Center for Cell-Matrix Research University of Oulu Oulu Finland Faculty of Biochemistry and Molecular Medicine University of Oulu Oulu Finland Biocenter Oulu University of Oulu Oulu Finland Molecular AI Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Division of Systems and Synthetic Biology Department of Life Sciences Chalmers University of Technology Gothenburg Sweden Research and Early Development Respiratory & Immunology BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden

Vascular anomalies (VA) refer to abnormal blood or lymphatic vessel architecture, most often as a result of dysregulated growth. Venous malformations (VM), a subgroup of VAs, are triggered by activating mutations in the Angiopoietin/TIE2-PI3K/AKT/mTOr signaling pathway with TIE2 L914F (gene name TEK) being one of the most frequent mutations in patients with VMs. Although systemic targeting of the overactivated pathway is possible, it would be a therapeutic advantage to restrict treatment to only the affected lesions. To identify peptides with potential selective binding to TIE2 L914F lesions we applied in vivo phage display to TIE2 L914F-overexpressing endothelial cells (ECs) in a subcutaneous matrigel xenograft mouse model of VMs. By panning for lesion-targeting phages in combination with subcellular fractionation, a screen for cell-penetrating candidate phages was established. Employing Next Generation Sequencing (NGS) and a refined bioinformatic analysis we were able to identify many novel cell-penetrating peptides (CPPs). To pinpoint the most selective and viable CCP candidates a hierarchical clustering algorithm was utilized. This method aggregated CPPs with highly similar sequences into a small number of clusters from which consensus sequences could be derived. Selected candidate CPPs exhibited uptake in TIE2 L914F-expressing human umbilical vein endothelial cells (HUVEC) in culture and were able to deliver sirNA into these cells. In conclusion, our NGS bioinformatic-supported approach led to the identification of novel and selective CPPs capable of transporting a sirNA cargo into targeted cells. © 2024

关键词： Bioinformatic analysis Cell-penetrating peptides In vivo phage display Next generation sequencing Vascular anomalies

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Machine Learning With data Assimilation and Uncertainty Quantification for dynamical Systems:A review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo ronan Fablet didier Lucor Bertrand Iooss Julien Brajard dunhui Xiao Tijana Janjic Weiping ding Yike Guo Alberto Carrassi Marc Bocquet rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(dA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFd)to geoscience and climate ***,much effort has been given in combining dA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply dA and UQ techniques to improve the accuracy and the interpretability of their models,but also at dA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of dA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(dA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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